The most common systems to detect money laundering based on transaction data employed by banks are rule based [1]}. In this work, we tackle the issue of reducing false positives of such systems, therefore keeping the rules for compliance and explainability reasons. In contrast, most of the existing works discussed below replace the rule-based system entirely and try to tackle two problems at once: reducing false positives on the one hand and reducing false negatives on the other hand. For thorough reviews of machine learning approaches for AML, we refer the reader to [2]}, [3]}.
